{"id":"W2018548991","doi":"10.1088/0964-1726/20/12/125009","title":"Decentralized modal identification using sparse blind source separation","year":2011,"lang":"en","type":"article","venue":"Smart Materials and Structures","topic":"Structural Health Monitoring Techniques","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wavelet; Sparse approximation; Modal; Wavelet packet decomposition; Blind signal separation; Computer science; Algorithm; Process (computing); Wavelet transform; Stationary wavelet transform; Second-generation wavelet transform; Discrete wavelet transform; Mathematics; Pattern recognition (psychology); Artificial intelligence; Telecommunications","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009872061,0.0001449352,0.0001641129,0.00006473027,0.00009872155,0.00007907896,0.00007689781,0.0001067993,0.0001135578],"category_scores_gemma":[0.000009741474,0.0001323555,0.00001667791,0.00005154324,0.0000340838,0.0001499368,0.00002147918,0.00005125285,0.000002575582],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003380028,"about_ca_system_score_gemma":0.000007624093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002240362,"about_ca_topic_score_gemma":0.000007309126,"domain_scores_codex":[0.9992408,0.00003382325,0.0002780647,0.0001528077,0.00009190421,0.0002026734],"domain_scores_gemma":[0.9996873,0.000007678302,0.00005744231,0.0001575159,0.00002728116,0.00006280535],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001711975,0.000004742732,0.002196009,0.0002611141,0.00003727711,0.00000309624,0.001822219,0.0003440788,0.984778,0.001793743,0.0002297141,0.008358847],"study_design_scores_gemma":[0.0002753017,0.00001867304,0.1467471,0.00002104738,0.00002420546,0.0000268628,0.00003030242,0.001569992,0.8402823,0.01041134,0.0003770562,0.0002158288],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964768,0.0001362627,0.001481637,0.000003607608,0.001296948,0.0002165772,0.0000157606,0.0003265206,0.00004584082],"genre_scores_gemma":[0.9923109,0.00008483564,0.007364293,0.000008922335,0.0001715478,0.00001040413,0.0000148591,0.00002567913,0.000008512457],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.144551,"threshold_uncertainty_score":0.5397303,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0504086163574549,"score_gpt":0.3052971837889429,"score_spread":0.254888567431488,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}